Achieving significant (e.g., 5%, 10%, 25%, 30% or more) savings in the power needed to perform various signal processing tasks including, e.g., critical surveillance related functions, is an important goal. Savings in processor power may be achieved through innovative power-efficient microprocessor technology, and also by achieving improved utilization of the new power-efficient multicore signal processing environments that can perform embedded real-time signal-processing. Many applications, such as Electronic Intelligence (ELINT), processing of radar signals, processing of cellular communication signals, etc., are data intensive requiring acquisition and processing of hundreds of megabytes, a few gigabytes, or even more amounts of data in time periods as small as a few or tens of microseconds. Increasing computing power and/or using faster processors is one way to accomplish such processing. Additional power savings may be achieved through improvements in sensors and associated signal processing.
Another approach is compressive sensing. Compressive sensing, in general, is a technique for reducing the bandwidth required for a signal, at the expense of more complex (and potentially power hungry) sparse optimization. As such, using compressive sensing, the amount of data acquired and/or processed can be reduced, though, the processing required generally increases. Therefore, using compressive sensing, though beneficial or even necessary in some situations, can adversely affect the goal of saving energy/power.
In certain electro-magnetic signal processing applications (e.g., electronic intelligence (ELINT) applications, radar systems, cellular communication applications, etc.), where both power savings and performance may be important, it is necessary to detect and locate signals covering a wide bandwidth, and originating from a few sources in the far field of a linear phased array. For short coherent dwells, it is observed that these signals typically occupy a sparse set of frequencies, reflecting the fact that transmitters hop among frequencies and therefore occupy only a small subset of the full band at any given time. The signals also occupy a sparse set of angles, corresponding to the fact that there are typically only a few active transmitters.
One approach is to try to locate the angles of the transmitters first, then apply wideband processing at those angles to retrieve the spectra. Another approach is to perform sparse reconstruction in the frequency domain, then perform narrowband angle determination only at the narrow bands that show up in the spectrum. In some situations, these approaches are too slow. Moreover, the energy consumed in this two-step techniques can make them unsuitable for some applications.